optimal_learning_rate.ipynb

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  "metadata": {
    "colab": {
      "name": "optimal_learning_rate.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNX1e0L2P+jUtm52OPyxlPZ",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Aayushktyagi/DeepLearning_Resources/blob/master/optimal_learning_rate.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hfZpxcosy1Ie",
        "outputId": "eed529e4-bd06-4c4c-e2ca-0e3abd7de080"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UsMOO-0tzBAb"
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "import os\n",
        "import math\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import datasets\n",
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Dropout, Flatten, Input\n",
        "from tensorflow.keras.optimizers import SGD, Adam\n",
        "from tensorflow.keras.models import Sequential, Model\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras.callbacks import Callback, ModelCheckpoint, EarlyStopping, History\n",
        "from tensorflow.keras import backend as K"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "apu9YTarzBzK"
      },
      "source": [
        "# Reproducibility is important\n",
        "seed = 1234\n",
        "np.random.seed(seed)\n",
        "tf.random.set_seed(seed)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7sU_6BNkzFZB"
      },
      "source": [
        "%matplotlib inline\n",
        "%config InlineBackend.figure_format=\"svg\""
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AleeS1NIzGJX"
      },
      "source": [
        "class LRFinder(Callback):\n",
        "    \"\"\"\n",
        "    Learning rate finder. This is a part of 1-cycle policy where we try to find the \n",
        "    optimal learning rate for training a model. \n",
        "    \"\"\"\n",
        "    def __init__(self, \n",
        "                 base_lr=1e-5,\n",
        "                 max_lr=10,\n",
        "                 beta=0.98,\n",
        "                 scale_min_loss=None,\n",
        "                 nb_steps=None,\n",
        "                 clip_initial_values=None,\n",
        "                 clip_end_values=None):\n",
        "        \"\"\"\n",
        "        Arguments:\n",
        "            base_lr: Initial learning rate. Can be as low as 1e-8\n",
        "            \n",
        "            max_lr:  Maximum learning rate to be considered for the test. Although `high learning rate` \n",
        "                     is a model dependent thing. For example, for a 3-layer network 0.1 can be a pretty high \n",
        "                     learning rate. A value between 2-10 is fine and if you wish you can run the learning rate \n",
        "                     test for more than one maximum value.\n",
        "            \n",
        "            beta:     Smoothening parameter for moving average of loss values. Better not to change the default value.\n",
        "            nb_steps: Number of training steps/iterations in an epoch\n",
        "            scale_min_loss: A value that is used to scale the minimum loss found yet to compare it with the\n",
        "                            current loss. A value between 3-5 is found to be nominal \n",
        "            \n",
        "            clip_initial_values: How many initial values to clip in the plots?\n",
        "            clip_end_values: How many values to clip from the end in the plots?\n",
        "        \"\"\"\n",
        "        super().__init__()\n",
        "        assert nb_steps > 1, \"Number of training steps should be greateer than 1\"\n",
        "        \n",
        "        self.base_lr = base_lr\n",
        "        self.max_lr = max_lr\n",
        "        self.beta = beta\n",
        "        self.nb_steps = nb_steps\n",
        "        self.min_loss = 0\n",
        "        self.batch_num=0\n",
        "        self.avg_loss=0\n",
        "        self._lr_scaler = (max_lr / base_lr)**(1/(nb_steps-1))\n",
        "        self.min_loss_scaler = scale_min_loss\n",
        "        self.clip_initial_values = clip_initial_values\n",
        "        self.clip_end_values = clip_end_values\n",
        "        self.history = {}\n",
        "    \n",
        "    \n",
        "    @property\n",
        "    def lr_scaler(self):\n",
        "        return (self.max_lr / self.base_lr)**(1/(self.nb_steps-1))\n",
        "    \n",
        "    \n",
        "    def on_train_begin(self, logs={}):\n",
        "        logs = logs or {}\n",
        "        if self.batch_num == 0:\n",
        "            K.set_value(self.model.optimizer.lr, self.base_lr)\n",
        "            \n",
        "    def on_batch_begin(self, epoch, logs=None):\n",
        "        self.batch_num+=1\n",
        "        for k, v in logs.items():\n",
        "            self.history.setdefault(k, []).append(v)\n",
        "        \n",
        "    \n",
        "    def on_batch_end(self, epoch, logs=None):\n",
        "        logs = logs or {}\n",
        "        loss = logs[\"loss\"]\n",
        "        self.avg_loss = self.beta*self.avg_loss + (1-self.beta)*loss\n",
        "        loss_smoothened = self.avg_loss/(1-self.beta**self.batch_num)\n",
        "        \n",
        "        if self.batch_num==1 or loss_smoothened < self.min_loss:\n",
        "            self.min_loss = loss_smoothened\n",
        "            \n",
        "        self.history.setdefault('batch_loss', []).append(loss_smoothened)\n",
        "        self.history.setdefault('lr', []).append(math.log10(self.base_lr))\n",
        "        \n",
        "        if self.min_loss_scaler:\n",
        "            max_loss_check = self.min_loss_scaler*self.min_loss\n",
        "        else:\n",
        "            max_loss_check = 4*self.min_loss\n",
        "            \n",
        "        if self.batch_num>1 and (np.isnan(loss_smoothened) \n",
        "                                 or loss_smoothened > max_loss_check):\n",
        "            print(\"LR range test complete\")\n",
        "            self.model.stop_training=True\n",
        "            self.plot_values(self.history['lr'], self.history['batch_loss'])\n",
        "        \n",
        "        for k, v in logs.items():\n",
        "            self.history.setdefault(k, []).append(v)\n",
        "        \n",
        "        self.base_lr*=self.lr_scaler\n",
        "        K.set_value(self.model.optimizer.lr, self.base_lr)\n",
        "        \n",
        "    \n",
        "    def plot_values(self,x,y):\n",
        "        f,ax = plt.subplots(1,1, figsize=(8,5))\n",
        "        if self.clip_initial_values:\n",
        "            x = x[self.clip_initial_values:]\n",
        "            y = x[self.clip_initial_values:]\n",
        "        \n",
        "        if self.clip_end_values:\n",
        "            x = x[:self.clip_end_values]\n",
        "            y = y[:self.clip_end_values]\n",
        "        \n",
        "        ax.plot(x, y)\n",
        "        plt.title(\"LR_range test\")\n",
        "        plt.xlabel(\"Learning rate on log10 scale\")\n",
        "        plt.ylabel(\"Loss\")\n",
        "        plt.show()"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fnqJ1-8FzKKy",
        "outputId": "72d59420-9768-4f35-ffa3-e10d36ba52c0"
      },
      "source": [
        "# Load the MNIST dataset\n",
        "(x_train, y_train), (x_test, y_test) = datasets.mnist.load_data()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sWr4wHdwzM1H",
        "outputId": "42898d7d-5708-42e6-f334-4b9ea4f01267"
      },
      "source": [
        "# Pre-process the dataset\n",
        "img_rows, img_columns, img_channels = 28,28,1\n",
        "nb_classes=10\n",
        "\n",
        "x_train = x_train.astype('float32')\n",
        "x_test = x_test.astype('float32')\n",
        "x_train /= 255\n",
        "x_test /= 255\n",
        "print('x_train shape:', x_train.shape)\n",
        "print(x_train.shape[0], 'train samples')\n",
        "print(x_test.shape[0], 'test samples')\n",
        "\n",
        "# Reshape the samples in the dataset as required by the model\n",
        "x_train = x_train.reshape(x_train.shape[0], img_rows, img_columns, img_channels)\n",
        "x_test = x_test.reshape(x_test.shape[0], img_rows, img_columns, img_channels)\n",
        "\n",
        "# convert class vectors to one hot-encoded vectors\n",
        "y_train = to_categorical(y_train, nb_classes)\n",
        "y_test = to_categorical(y_test, nb_classes)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "x_train shape: (60000, 28, 28)\n",
            "60000 train samples\n",
            "10000 test samples\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XJnHHqI-zNt6"
      },
      "source": [
        "def build_simple_model(input_shape, opt=None):\n",
        "    model = Sequential()\n",
        "    model.add(Conv2D(32, kernel_size=(3, 3),\n",
        "                     activation='relu',\n",
        "                     input_shape=input_shape))\n",
        "    model.add(Conv2D(64, (3, 3), activation='relu'))\n",
        "    model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "    model.add(Dropout(0.25))\n",
        "    model.add(Flatten())\n",
        "    model.add(Dense(128, activation='relu'))\n",
        "    model.add(Dropout(0.5))\n",
        "    model.add(Dense(10, activation='softmax'))\n",
        "    \n",
        "    if opt:\n",
        "        opt = opt\n",
        "    else:\n",
        "        opt = SGD()\n",
        "    \n",
        "    model.compile(loss='categorical_crossentropy',\n",
        "                  optimizer=opt,\n",
        "                  metrics=['accuracy'])\n",
        "    return model"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ze5tR54BzQnM"
      },
      "source": [
        "# Define batch size, number of epochs and number of training steps in an epoch\n",
        "batch_size=32\n",
        "nb_epochs =10\n",
        "nb_steps = len(x_train)//batch_size\n",
        "input_shape = (img_rows,img_columns,img_channels)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zq46nkBLzVDS",
        "outputId": "4c10b51d-943e-4269-c67f-8a97869f057b"
      },
      "source": [
        "# Build the model\n",
        "model = build_simple_model(input_shape)\n",
        "\n",
        "# Train the model\n",
        "model.fit(x_train, y_train, epochs=nb_epochs, batch_size=batch_size)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 168s 89ms/step - loss: 0.5765 - accuracy: 0.8188\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 167s 89ms/step - loss: 0.2964 - accuracy: 0.9104\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 169s 90ms/step - loss: 0.2317 - accuracy: 0.9305\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 166s 89ms/step - loss: 0.1826 - accuracy: 0.9455\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 166s 88ms/step - loss: 0.1477 - accuracy: 0.9557\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 166s 89ms/step - loss: 0.1256 - accuracy: 0.9628\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 168s 90ms/step - loss: 0.1121 - accuracy: 0.9654\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 167s 89ms/step - loss: 0.0989 - accuracy: 0.9700\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 166s 88ms/step - loss: 0.0910 - accuracy: 0.9726\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 166s 89ms/step - loss: 0.0858 - accuracy: 0.9740\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f4c41a761d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4vRgc5XJzXcM",
        "outputId": "8b1d4ff8-c39d-44b9-81e7-0070a5b55c2d"
      },
      "source": [
        "# What is the accuracy for this model?\n",
        "score = model.evaluate(x_test, y_test, verbose=0, batch_size=batch_size)\n",
        "print('Test loss:', score[0])\n",
        "print('Test accuracy:', score[1])"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test loss: 0.04657870903611183\n",
            "Test accuracy: 0.9848999977111816\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "tIEowQeNzZrG",
        "outputId": "dca614b7-cdf5-4119-9b9a-38df89a82122"
      },
      "source": [
        "# Get an instance of LRTest class\n",
        "lr_finder = LRFinder(nb_steps=nb_steps)\n",
        "\n",
        "# Build the model again\n",
        "model2 = build_simple_model(input_shape)\n",
        "\n",
        "# Perform the LR range test \n",
        "history = model2.fit(x_train, y_train, epochs=nb_epochs, batch_size=batch_size, verbose=0, callbacks=[lr_finder])"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "LR range test complete\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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            "tags": [],
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "fQT2gEYczabj",
        "outputId": "d2cfa970-2d6f-41bf-c2c1-553f9a21ff62"
      },
      "source": [
        "lrs, losses = (lr_finder.history[\"lr\"], lr_finder.history[\"batch_loss\"])\n",
        "_, ax = plt.subplots(1,1)\n",
        "ax.plot(lrs, losses)\n",
        "ax.set_ylabel(\"Loss\")\n",
        "ax.set_xlabel(\"Learning Rate\")\n",
        "#ax.set_xscale('log10')\n",
        "#ax.xaxis.set_major_formatter(plt.FormatStrFormatter('%.0e'))"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Learning Rate')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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